Optimal bowel preparation is a prerequisite for a successful colonoscopy; however, the rate of inadequate bowel preparation remains relatively high. In this study, we establish a smartphone app that assesses patient bowel preparation using an artificial intelligence (AI)-based prediction system trained on labeled photographs of feces in the toilet and evaluate its impact on bowel preparation quality in colonoscopy outpatients. We conduct a prospective, single-masked, multicenter randomized clinical trial, enrolling outpatients who own a smartphone and are scheduled for a colonoscopy. We screen 578 eligible patients and randomize 524 in a 1:1 ratio to the control or AI-driven app group for bowel preparation. The study endpoints are the percentage of patients with adequate bowel preparation and the total BBPS score, compliance with dietary restrictions and purgative instructions, polyp detection rate, and adenoma detection rate (secondary). The prediction system has an accuracy of 95.15%, a specificity of 97.25%, and an area under the curve of 0.98 in the test dataset. In the full analysis set (n = 500), adequate preparation is significantly higher in the AI-driven app group (88.54 vs. 65.59%; P < 0.001). The mean BBPS score is 6.74 ± 1.25 in the AI-driven app group and 5.97 ± 1.81 in the control group (P < 0.001). The rates of compliance with dietary restrictions (93.68 vs. 83.81%, P = 0.001) and purgative instructions (96.05 vs. 84.62%, P < 0.001) are significantly higher in the AI-driven app group, as is the rate of additional purgative intake (26.88 vs. 17.41%, P = 0.011). Thus, our AI-driven smartphone app significantly improves the quality of bowel preparation and patient compliance.

Purpose This study explored the oncological and obstetric results of radical trachelectomy (RT) in early-stage cervical cancer patients. Methods A retrospective analysis was conducted the oncological and obstetric results of 23 patients with early cervical cancer (stages IA2–IB3; International Federation of Gynecology and Obstetrics, 2018) who underwent RT in The Maternal and Child Health Care Hospital of Guiyang, China, from October 2004 to September 2018. Results 23 patients had cervical tumors of the squamous cell carcinoma histological type. All 23 patients retained reproductive function. The mean follow-up time was 112.87 ± 55.75 (36–199) months. The median tumor size was 2.00 ± 1.35 cm (imperceptible to the eyes 5.00 cm). No recurrence was observed in any of the patient cases. Among the patients with a tumor size > 4 cm (up to 5 cm), three patients who wished to preserve fertility accepted RT following neoadjuvant chemotherapy The pregnancy outcomes were as follows: 8 cases (47.06%) out of 17 cases who attempting pregnancy conceived 12 times.First-trimester abortion and the voluntary abandonment of pregnancy occurred in 4 cases (33.33%), respectively, one patient performed deliberate termination at 24 weeks of gestation. Second-trimester abortion occurred in three cases (25.0%) for chorioamnionitis. Premature delivery at 32 weeks occurred in one case (8.33%). Conclusion Radical trachelectomy is a safe and effective treatment for women with early-stage cervical cancer preserving fertility biology. Patients with a cervical tumor sized > 4 cm can be pregnant after neoadjuvant chemotherapy and RT. Accordingly, this treatment is worthy of further exploration.

BackgroundEndoscopic biopsy is the pivotal procedure for the diagnosis of gastric cancer. In this study, we applied whole-slide images (WSIs) of endoscopic gastric biopsy specimens to develop an endoscopic gastric biopsy assistant system (EGBAS).MethodsThe EGBAS was trained using 2373 WSIs expertly annotated and internally validated on 245 WSIs. A large-scale, multicenter test dataset of 2003 WSIs was used to externally evaluate EGBAS. Eight pathologists were compared with the EGBAS using a man-machine comparison test dataset. The fully manual performance of the pathologists was also compared with semi-manual performance using EGBAS assistance.ResultsThe average area under the curve of the EGBAS was 0·979 (0·958-0·990). For the diagnosis of all four categories, the overall accuracy of EGBAS was 86·95%, which was significantly higher than pathologists (P&lt; 0·05). The EGBAS achieved a higher κ score (0·880, very good κ) than junior and senior pathologists (0·641 ± 0·088 and 0·729 ± 0·056). With EGBAS assistance, the overall accuracy (four-tier classification) of the pathologists increased from 66·49 ± 7·73% to 73·83 ± 5·73% (P&lt; 0·05). The length of time for pathologists to manually complete the dataset was 461·44 ± 117·96 minutes; this time was reduced to 305·71 ± 82·43 minutes with EGBAS assistance (P = 0·00).ConclusionsThe EGBAS is a promising system for improving the diagnosis ability and reducing the workload of pathologists.